WEB-REPORT
Authentication: We declare that the web-report is our own work and does not contain plagiarised material
Group 9-14
Project Title Credit Card Fraud Detection
Synopsis With the advent of the internet and computers comes new and easier ways of doing things, like buying items and products online using one's credit card. With a simple click of some buttons and keystrokes, anyone could buy anything in the comfort of their homes. In this brave new world of convenience and accessibility, internet fraud is on the rise. Therefore, it is essential that people tackle this pertinent issue. Without improved security measures, the fraudsters will effectively win. It is a constant tug of war and we need to have the upper hand to defeat them.

Meanwhile, strides have been made in machine learning, a process whereby a computer absorbs information and data and uses them to handle new information, and make decisions from that. With constant streams of information flowing in at all times, data is getting too big for humans to handle. This is where machines step in. With automated systems utilising machine learning to sort data, humans can focus on other things.

By utilising machine learning on credit card fraud detection, we aim to combat the prevalent threat of credit card fraud by identifying it with computers. Through obtaining data from various credit card purchases and histories, the program will learn and be able to detect anomalies and potential frauds without telling it explicitly where to look. Machine learning also allows for more flexibility in detecting frauds, as it can adapt to new information with ease and keep up with the new tactics that fraudsters employ. (250 words)

(By the way we feel that this synopsis page design is not the most ideal, so we have an alternative HERE, which not only looks better but works across all screen sizes.)
Link to start page Click HERE to access web-report
Special instructions for
evaluator to take note
Please avoid using browsers made by Microsoft.
Team Members
(Names & Classes)
Group Leader:
SHI ZIYUAN 3S2
 
Group Member/s:
CAI ZHOUXUAN 3S3
CHENG JIN KIT, LLOYD 3S3